Engineering Leader

Responsibilities

Scalable Multi-Tenant Systems

• Architect and build multi-tenant systems serving enterprise customers across geographies with strict data residency, isolation, and compliance requirements.
• Design high-throughput ingestion systems for large volumes of voice data, call metadata, and operational telemetry in near real time.
• Make architectural decisions about data stores, stream processing, and storage tiers, balancing query performance, cost, and operational simplicity.
• Champion SOLID principles, clean architecture, and engineering rigor across codebases.

Team Building and Engineering Culture

• Recruit, hire, and develop senior engineers across voice engineering, backend systems, data engineering, and applied operations research.
• Establish engineering standards, code review culture, and a strong bias toward shipping while maintaining system reliability and customer experience.
• Coach senior and staff engineers into technical leaders and manage engineering managers as the organization scales.
• Drive cross-functional alignment with Product, ML/AI, and Go-to-Market teams.

Voice Platform and Agent QA

• Own end-to-end engineering for Agent QA capabilities, including real-time voice pipelines, telephony integrations, and voice-to-action workflows.
• Build and scale automated call scoring, compliance monitoring, sentiment analysis, and coaching feedback loops.
• Design auto-learning systems with closed-loop feedback, model retraining triggers, and quality regression detection.
• Deliver voice analytics including call-volume trends, handle-time distributions, first-call resolution metrics, agent-performance dashboards, and anomaly detection.
• Build scalable voice pipelines across geographies while addressing PII, security, and compliance requirements.

Workforce Management and Operations Research

• Build workforce-management capabilities including demand forecasting, shift scheduling, real-time adherence monitoring, and capacity planning.
• Design optimization algorithms for headcount modeling, skill-based routing, and workload balancing across multi-site, multi-time-zone contact-center operations.
• Partner with ML/AI teams to integrate predictive models for call-volume forecasting, attrition risk, and staffing efficiency.

Requirements

• 12+ years of software engineering experience, including 4+ years leading engineering teams of 8+ engineers at high-growth startups or top-tier technology companies.
• Deep expertise building real-time, data-intensive systems.
• Strong foundation in relational databases, time-series stores, columnar analytics engines, and caching layers.
• Experience building quality software using AI-driven tools and workflows.
• Track record taking products from 0 to 1 and iterating rapidly based on customer feedback.
• Hands-on proficiency with SOLID principles, clean architecture, and design patterns.
• Experience building scalable, multi-tenant SaaS platforms with data isolation, geo-distributed deployments, and compliance requirements such as SOC 2, GDPR, and HIPAA.
• Experience building high-volume ingestion systems using event-driven architectures, stream processing, and hybrid batch/real-time pipelines.
• Strong product sense and ability to translate contact-center, voice, and workforce-domain problems into technical solutions.
• Proven ability to hire, develop, and retain high-caliber engineers across multiple disciplines.

Nice-to-Haves

• Voice or telephony systems experience, including WebRTC, SIP, VoIP infrastructure, or contact-center platforms.
• Operations research or optimization experience, including scheduling algorithms, linear or integer programming, constraint satisfaction, or demand forecasting models.
• Experience building analytics platforms, OLAP systems, real-time dashboards, metric computation engines, or BI infrastructure.
• Experience with auto-learning or continuous-improvement systems, automated retraining pipelines, or quality-monitoring systems.
• Experience with AI/ML-powered enterprise products, agentic automation, LLM orchestration, or AI-driven UX.
• Familiarity with workforce management or contact-center operations, including staffing models, Erlang-based capacity planning, or real-time adherence systems.

Compensation and Benefits

• Competitive compensation, meaningful equity, and benefits.
• Compensation is determined by location, level, job-related knowledge, skills, and experience.
• Certain roles may be eligible for variable compensation, equity, and benefits.